from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
/tmp/ipykernel_121051/3777615979.py:1: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display from IPython.core.display import display, HTML
import pandas as pd
import numpy as np
import plotly
import matplotlib.pyplot as plt
import sklearn
import plotly.graph_objects as go
import plotly.express as px
pd.options.display.max_columns = 100
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split, KFold, cross_val_score
from datetime import datetime
from catboost import Pool, CatBoostClassifier, cv
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings("ignore")
org_data = pd.read_csv('sample_data.csv', sep=';')
## Copy the original data to another dataframe which can be modified later
preprocess_data = org_data.copy()
preprocess_data
| age | job | marital | education | default | balance | housing | loan | contact | day | month | duration | campaign | pdays | previous | poutcome | y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 58 | management | married | tertiary | no | 2143 | yes | no | unknown | 5 | may | 261 | 1 | -1 | 0 | unknown | no |
| 1 | 44 | technician | single | secondary | no | 29 | yes | no | unknown | 5 | may | 151 | 1 | -1 | 0 | unknown | no |
| 2 | 33 | entrepreneur | married | secondary | no | 2 | yes | yes | unknown | 5 | may | 76 | 1 | -1 | 0 | unknown | no |
| 3 | 47 | blue-collar | married | unknown | no | 1506 | yes | no | unknown | 5 | may | 92 | 1 | -1 | 0 | unknown | no |
| 4 | 33 | unknown | single | unknown | no | 1 | no | no | unknown | 5 | may | 198 | 1 | -1 | 0 | unknown | no |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 45206 | 51 | technician | married | tertiary | no | 825 | no | no | cellular | 17 | nov | 977 | 3 | -1 | 0 | unknown | yes |
| 45207 | 71 | retired | divorced | primary | no | 1729 | no | no | cellular | 17 | nov | 456 | 2 | -1 | 0 | unknown | yes |
| 45208 | 72 | retired | married | secondary | no | 5715 | no | no | cellular | 17 | nov | 1127 | 5 | 184 | 3 | success | yes |
| 45209 | 57 | blue-collar | married | secondary | no | 668 | no | no | telephone | 17 | nov | 508 | 4 | -1 | 0 | unknown | no |
| 45210 | 37 | entrepreneur | married | secondary | no | 2971 | no | no | cellular | 17 | nov | 361 | 2 | 188 | 11 | other | no |
45211 rows × 17 columns
The dataset contains total 45,211 observations and 17 features (16 independent and 1 dependent)
preprocess_data.isnull().sum()
age 0 job 0 marital 0 education 0 default 0 balance 0 housing 0 loan 0 contact 0 day 0 month 0 duration 0 campaign 0 pdays 0 previous 0 poutcome 0 y 0 dtype: int64
No null values are present in any of the feature.
# fig = fig
fig = px.box(preprocess_data, y="age", width=400, height = 500)
fig.show()
## Check the number of outiers
age_out = preprocess_data[preprocess_data['age']>70].shape[0]
age_out_perc = round((age_out/preprocess_data.shape[0])*100,2)
print("Total {} are outliers which is around {}% of total observations. \nSince the % is quite low, we can drop these observations from our analysis".format(age_out, age_out_perc))
preprocess_data = preprocess_data[preprocess_data['previous']<=70]
Total 487 are outliers which is around 1.08% of total observations. Since the % is quite low, we can drop these observations from our analysis
preprocess_data.balance.value_counts(dropna=False)
0 3514
1 195
2 156
4 139
3 134
...
-381 1
4617 1
20584 1
4358 1
16353 1
Name: balance, Length: 7168, dtype: int64
fig = fig
fig = px.box(preprocess_data, y="balance", width=400, height = 500)
fig.show()
balance_out = preprocess_data[preprocess_data['balance']>3462].shape[0]
balance_out_perc = round((balance_out/preprocess_data.shape[0])*100,2)
balance_out, balance_out_perc
(4712, 10.42)
## Replace the outlier values with the mean of the columns
mean_balance = preprocess_data['balance'].mean()
preprocess_data.loc[(preprocess_data['balance']>3462), 'balance'] = mean_balance
preprocess_data.loc[(preprocess_data['balance']<-1854), 'balance'] = mean_balance
preprocess_data.duration.value_counts(dropna=False).sort_index()
0 3
1 2
2 3
3 4
4 15
..
3366 1
3422 1
3785 1
3881 1
4918 1
Name: duration, Length: 1573, dtype: int64
## Duration
fig = fig
fig = px.box(preprocess_data, y="duration", width=400, height = 500)
fig.show()
duration_out = preprocess_data[preprocess_data['duration']>643].shape[0]
duration_out_perc = round((duration_out/preprocess_data.shape[0])*100,2)
duration_out, duration_out_perc
(3235, 7.16)
## Replace the outlier values with the mean of the columns
mean_duration = preprocess_data['duration'].mean()
preprocess_data.loc[(preprocess_data['duration']>643), 'duration'] = mean_duration
preprocess_data.campaign.value_counts(dropna=False).sort_index()
1 17544 2 12504 3 5521 4 3522 5 1764 6 1291 7 735 8 540 9 327 10 266 11 201 12 155 13 133 14 93 15 84 16 79 17 69 18 51 19 44 20 43 21 35 22 23 23 22 24 20 25 22 26 13 27 10 28 16 29 16 30 8 31 12 32 9 33 6 34 5 35 4 36 4 37 2 38 3 39 1 41 2 43 3 44 1 46 1 50 2 51 1 55 1 58 1 63 1 Name: campaign, dtype: int64
## Campaign
fig = fig
fig = px.box(preprocess_data, y="campaign", width=400, height = 500)
fig.show()
campaign_out = preprocess_data[preprocess_data['campaign']>6].shape[0]
campaign_out_perc = round((campaign_out/preprocess_data.shape[0])*100,2)
campaign_out, campaign_out_perc
(3064, 6.78)
## Replace the outlier values with the mean of the columns
mean_campaign = preprocess_data['campaign'].mean()
preprocess_data.loc[(preprocess_data['campaign']>6), 'campaign'] = mean_campaign
preprocess_data.previous.value_counts()
0 36954 1 2772 2 2106 3 1142 4 714 5 459 6 277 7 205 8 129 9 92 10 67 11 65 12 44 13 38 15 20 14 19 17 15 16 13 19 11 20 8 23 8 18 6 22 6 24 5 27 5 21 4 29 4 25 4 30 3 38 2 37 2 26 2 28 2 51 1 58 1 32 1 40 1 55 1 35 1 41 1 Name: previous, dtype: int64
fig = fig
fig = px.box(preprocess_data, y="previous", width=400, height = 500)
fig.show()
## Check the number of outiers
previous_out = preprocess_data[preprocess_data['previous']>58].shape[0]
previous_out_perc = round((previous_out/preprocess_data.shape[0])*100,4)
print("Total {} are outliers which is around {}% of total observations. \nSince the % is quite low, we can drop these observations from our analysis".format(previous_out, previous_out_perc))
preprocess_data = preprocess_data[preprocess_data['previous']<=58]
Total 0 are outliers which is around 0.0% of total observations. Since the % is quite low, we can drop these observations from our analysis
temp1 = preprocess_data.day.value_counts().sort_index().reset_index(name = 'Count').rename(columns={'index':'Date'})
temp1
| Date | Count | |
|---|---|---|
| 0 | 1 | 322 |
| 1 | 2 | 1292 |
| 2 | 3 | 1079 |
| 3 | 4 | 1445 |
| 4 | 5 | 1910 |
| 5 | 6 | 1932 |
| 6 | 7 | 1817 |
| 7 | 8 | 1842 |
| 8 | 9 | 1561 |
| 9 | 10 | 524 |
| 10 | 11 | 1479 |
| 11 | 12 | 1603 |
| 12 | 13 | 1585 |
| 13 | 14 | 1848 |
| 14 | 15 | 1703 |
| 15 | 16 | 1415 |
| 16 | 17 | 1939 |
| 17 | 18 | 2308 |
| 18 | 19 | 1757 |
| 19 | 20 | 2752 |
| 20 | 21 | 2026 |
| 21 | 22 | 905 |
| 22 | 23 | 939 |
| 23 | 24 | 447 |
| 24 | 25 | 840 |
| 25 | 26 | 1035 |
| 26 | 27 | 1121 |
| 27 | 28 | 1830 |
| 28 | 29 | 1745 |
| 29 | 30 | 1566 |
| 30 | 31 | 643 |
## line plot on which day most number of calls are made
import plotly.express as px
fig = px.line(temp1, x='Date', y='Count', markers=True,width=1000, height = 400,)
fig.update_layout(
xaxis = dict(
tickmode = 'linear',
tick0 = 0,
dtick = 1
)
)
fig.show()
The company is making more number of calls majorly on 20th date in the given dataset.
preprocess_data.month = preprocess_data.month.str.upper()
d = {'JAN':1, 'FEB':2, 'MAR':3, 'APR':4, 'MAY':5, 'JUN':6,'JUL':7, 'AUG':8, 'SEP':9, 'OCT':10, 'NOV':11, 'DEC':12}
preprocess_data['month_number'] = preprocess_data.month.map(d)
temp2 = preprocess_data.groupby(['month','month_number']).size().reset_index(name='Count').sort_values('month_number')
temp2
| month | month_number | Count | |
|---|---|---|---|
| 4 | JAN | 1 | 1403 |
| 3 | FEB | 2 | 2648 |
| 7 | MAR | 3 | 477 |
| 0 | APR | 4 | 2932 |
| 8 | MAY | 5 | 13766 |
| 6 | JUN | 6 | 5341 |
| 5 | JUL | 7 | 6895 |
| 1 | AUG | 8 | 6247 |
| 11 | SEP | 9 | 579 |
| 10 | OCT | 10 | 738 |
| 9 | NOV | 11 | 3970 |
| 2 | DEC | 12 | 214 |
## line plot on which day most number of calls are made
import plotly.express as px
fig = px.line(temp2, x='month', y='Count', markers=True,width=1000, height = 400,)
fig.update_layout(
xaxis = dict(
tickmode = 'linear',
tick0 = 0,
dtick = 1
)
)
fig.show()
May has the highest number of calls, followed by July.
preprocess_data.shape
(45210, 18)
temp1 = preprocess_data.groupby('job').size().reset_index(name='Total')
temp2 = preprocess_data.groupby(['job','y']).size().reset_index(name = 'Count').rename(columns={'y':'Subscribe'})
temp3 = temp1.merge(temp2, on=['job'])
temp3['Percentage'] = round(temp3['Count']/temp3['Total']*100,2)
fig = px.bar(temp3, x="job", y="Percentage", color='Subscribe', barmode='group',width = 1200,
height=400, color_discrete_sequence=["#FC6955",'rgb(102,166,30)',], text = 'Percentage')
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.show()
The above plot contains the percentages of subscribers from each category of job. We can observe that, out of all calls made to each category, students have the highest percentage of subscription depending upon the calls made to student category.
Relation of feature Education with Target Feature.
temp1 = preprocess_data.groupby('education').size().reset_index(name='Total')
temp2 = preprocess_data.groupby(['education','y']).size().reset_index(name = 'Count').rename(columns={'y':'Subscribe'})
temp3 = temp1.merge(temp2, on=['education'])
temp3['Percentage'] = round(temp3['Count']/temp3['Total']*100,2)
fig = px.bar(temp3, x="education", y="Percentage", color='Subscribe', barmode='group',width = 800,
height=400, color_discrete_sequence=["#FC6955",'rgb(102,166,30)',], text = 'Percentage')
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.show()
As we can observe from the above bar plot, out of total calls made to customer having an education level of tertiary, only 15% (which is highest among other categories) subscribe to the term deposite.
temp1 = preprocess_data.groupby('marital').size().reset_index(name='Total')
temp2 = preprocess_data.groupby(['marital','y']).size().reset_index(name = 'Count').rename(columns={'y':'Subscribe'})
temp3 = temp1.merge(temp2, on=['marital'])
temp3['Percentage'] = round(temp3['Count']/temp3['Total']*100,2)
fig = px.bar(temp3, x="marital", y="Percentage", color='Subscribe', barmode='group',width = 800,
height=450, color_discrete_sequence=["#FC6955",'rgb(102,166,30)',], text = 'Percentage')
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.show()
Around 15% of the singles subscribe to the term deposit over call. While divorced and married stand at 2nd and 3rd with the numbers 12% and 10%.
temp1 = preprocess_data.groupby('month').size().reset_index(name='Total')
temp2 = preprocess_data.groupby(['month','y']).size().reset_index(name = 'Count').rename(columns={'y':'Subscribe'})
temp3 = temp1.merge(temp2, on=['month'])
temp3['Percentage'] = round(temp3['Count']/temp3['Total']*100,2)
temp3['month_number'] = temp3.month.map(d)
temp3 = temp3.sort_values('month_number')
fig = px.bar(temp3, x="month", y="Percentage", color='Subscribe', barmode='group',
height=500, color_discrete_sequence=["#FC6955",'rgb(102,166,30)',], text = 'Percentage')
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.show()
March is having the highest percentage of acceptence calls, followed by December and September with percentages 47 and 46 respectively.
def plot_correlation_map( df ):
corr = df.corr()
_ , ax = plt.subplots( figsize =( 12,8 ) )
cmap = sns.diverging_palette( 220 , 10 , as_cmap = True )
_ = sns.heatmap(
corr,
cmap = cmap,
square=True,
cbar_kws={ 'shrink' : .9 },
ax=ax,
annot = True,
annot_kws = { 'fontsize' : 12 }
)
plot_correlation_map(preprocess_data)
#preprocess_data.corr()
The above correlation plot shows relation between several numerical features. The highest correlation is shown by pdays and previous features.
temp1 = preprocess_data.y.value_counts().reset_index().rename(columns={'index':'Subscription Acceptence', 'y':'Count'},)
fig = px.bar(temp1, x="Subscription Acceptence", y="Count", text = 'Count', height = 500, width = 700,
color_discrete_sequence=['rgb(0,134,139)', 'rgb(231,63,116)'])
# fig.update_traces(marker_color='rgb(158,202,225)', marker_line_color='rgb(8,48,107)',
# marker_line_width=1.5, opacity=0.6)
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.show()
The number of observations of calls with the result NO is around 8 times higher than YES. This difference between the number of observations in the target feature is known as Imbalance Dataset.
For predictive analysis, we are going to use 2 machine learning models, Catboost and XGBoost.
->The reason behind using catboost is that since we have quite a number of categorical features, we have to use encoding to convert them to numerical features which is automatically handled by catboost.
-> When we have a lot o numerical columns, it is advised to use boosting algorithms like LGB or XGB and for bagging we can use Random Forest. After trying all three of the algorithms mentioned above, we came to a conclusion of using XGBoost because it's more accurate on unforseen data.
After getting results separately from Catboost and XGBoost, we have ensembled the results and have taken the average of the results to reach the final conclusion. We are using mean_absolute_error as the metric to calculate the accuracy.
preprocess_data.job = preprocess_data.job.str.replace('-','_')
## change Categorical Target column to Numerical.
preprocess_data.y = preprocess_data.y.str.replace('yes','1')
preprocess_data.y = preprocess_data.y.str.replace('no','0')
preprocess_data.y = preprocess_data.y.astype(int)
## Prepare a test data, which will work as unforseen data
test_data = preprocess_data.sample(4500).reset_index(drop=True)
train_data = preprocess_data.drop(test_data.index, axis=0).reset_index(drop=True)
## Segregate dependent variable from indepedent variables
train_data_X = train_data.drop(['y'],axis=1)
train_data_Y = train_data.y
test_data_X = test_data.drop(['y'],axis=1)
test_data_Y = test_data.y
cat_features = train_data_X.select_dtypes(include=['object']).columns.tolist()
X_train, X_test, y_train, y_test = train_test_split(test_data_X,test_data_Y,train_size=.85,random_state=1234)
model = CatBoostClassifier(eval_metric='Accuracy',use_best_model=True,random_seed=42, scale_pos_weight=5)
model.fit(X_train,y_train,cat_features=cat_features,eval_set=(X_test,y_test))
y_pred = model.predict(X_test)
print(1-mean_absolute_error(y_test, y_pred))
final_pred = model.predict_proba(test_data_X)
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total: 2.72s remaining: 0us bestTest = 0.8301886792 bestIteration = 74 Shrink model to first 75 iterations. 0.8533333333333333
## Sine XGBoost does not care about categorical eatures on its own, thereore we have to convert categorical features into numerical
cat_cols = preprocess_data.select_dtypes(include=['object']).columns.tolist()
dummy_num_data = pd.get_dummies(preprocess_data[cat_cols])
num_cols = preprocess_data.select_dtypes(exclude=['object'])
dummy_train_data = pd.concat([dummy_num_data, num_cols],axis=1)
## Prepare a test data, which will work as unforseen data
test_data = dummy_train_data.sample(4500).reset_index(drop=True)
train_data = dummy_train_data.drop(test_data.index, axis=0).reset_index(drop=True)
## Segregate dependent variable from indepedent variables
train_data_X = train_data.drop(['y'],axis=1)
train_data_Y = train_data.y
test_data_X = test_data.drop(['y'],axis=1)
test_data_Y = test_data.y
## XGBClassifier
## Using StandardScaler to standardize the features
sc = StandardScaler()
X = train_data_X
X = sc.fit_transform(X)
X = pd.DataFrame(X)
test_data = test_data_X
test_data = sc.fit_transform(test_data)
test_data = pd.DataFrame(test_data)
X_train, X_val, y_train, y_val = train_test_split(X,train_data_Y,train_size=.85,random_state=1234)
def train_and_predict(model,X_train,y_train,X_val,y_val,test_data):
model.fit(X_train, y_train)
y_pred = model.predict(X_val)
print(1-mean_absolute_error(y_val, y_pred))
return model.predict_proba(test_data)[:,1]
kf = KFold(n_splits=5, shuffle=True, random_state=3)
pred_arr = []
model = XGBClassifier(n_jobs=-1, random_state=42, n_estimators=500, scale_pos_weight = 5,)
for train_index, test_index in kf.split(X):
pred =train_and_predict(model,X.iloc[train_index],train_data_Y.iloc[train_index],X.iloc[test_index],train_data_Y.iloc[test_index],test_data)
pred_arr.append(pred)
[18:56:38] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. 0.8884794890690249 [18:56:45] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. 0.8849177106362073 [18:56:52] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. 0.8894620486366986 [18:57:00] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. 0.8888479489069024 [18:57:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. 0.8937607467452714
final_y_pred = (pred_arr[0]+pred_arr[1]+pred_arr[2]+pred_arr[3]+pred_arr[4]+final_pred[:,1])/6
final_y_pred
array([0.07555678, 0.05826938, 0.07859755, ..., 0.02231362, 0.43762432,
0.37395117])
accuracy = 1-mean_absolute_error(test_data_Y, final_y_pred)
print('The accuracy of the above emsembled model is {0}'.format(round(accuracy,2)))
The accuracy of the above emsembled model is 0.88